Neurocomputing for Vision Research
Neurocomputing techniques become more and more important in vision research.
A great deal of problems in vision research are mitigated through the
neurocomputing techniques, such as Bayesian inference, density modeling and
clustering, latent variable models, manifold learning, neural networks,
kernel machines, sampling techniques, semi-supervised learning, and subspace
methods.
The successes of the neurocomputing for vision research have been witnessed
by the last five years. For example, the Markov chain Monte Carlo has been
well applied for video tracking; the support vector machines combined with
sampling technique and active learning have been demonstrated to improve the
performance of relevance feedback in content based visual information
retrieval significantly; the linear discriminant analysis and its variants
have shown as the light to bright a way for face recognition; the graph cuts
have been successfully employed in image segmentation; the supervised tensor
learning has been utilized for image classification and biometric
application; and the semi-supervised learning has boomed in image and video
editing. There are just example evident parts of the combination of the two
fields, neurocomputing and vision research.
Elsevier Neurocomputing hunts for original research results for a Special
Issue on Neurocomputing for Vision Research. The goals of this special issue
are: 1) developing novel techniques in neurocomputing to target specific
problems in vision research, 2) defining new vision research problems, which
can be cleared up by techniques in neurocomputing, and 3) investigating new
techniques in neurocomputing to enhance the performances of problems in
vision research.
Manuscripts are solicited to address a wide range of topics in
neurocomputing for vision research, but not limit to the following:
- Biometrics
- Classification and clustering in vision
- Emerging techniques for vision research
- Motion analysis and recognition
- New techniques in neurocomputing, such as subspace methods, kernel
machines, semi supervised learning, manifold learning, etc.
- Visual cognition
- Visual information management
- Visual surveillance
- Industrial applications
Manuscripts (8-30 pages in the Neurocomputing publishing format) should be
submitted via the Electronic Editorial System, Elsevier:
http://ees.elsevier.com/neucom/
Guide for authors can be found:
http://authors.elsevier.com/GuideForAuthors.html?PubID=505628&dc=GFA
Important: when submitting your manuscript, at the step of
"Selecting an Article Type is Required for Submission."
please indicate:
"Special Issue: Neurocomputing for Vision Research"
Important Dates
Manuscript submission: 10 April2007
Preliminary results: 10 July 2007
Revised version: 10 August 2007
Notification: 10 November 2007
Final manuscripts due: 10 December 2007
Anticipated publication: Spring 2008
Guest editors:
Dacheng Tao University of London dacheng.tao At gmail.com
Xuelong Li University of London xuelong_li At ieee.org
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Dacheng TAO
Department of Computing
Hong Kong Polytechnic University
Hung Hom, Kowloon, Hong Kong.
Office: PQ 704, Building P
Phone: 00852 2766 7266
Fax: 00852 2764 2528
Home Page: http://www4.comp.polyu.edu.hk/~csdct/